Publications de Juan Ramón Troncoso-Pastoriza

Scalable and Privacy-Preserving Federated Principal Component Analysis

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Jean-Philippe Léonard Bossuat

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

IEEE COMPUTER SOC2023

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, David Jules Froelicher, Apostolos Pyrgelis, Joao André Gomes de Sá e Sousa, Jean-Philippe Léonard Bossuat, Sinem Sav

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

2022

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Erman Ayday, Apostolos Pyrgelis

2022

Efficient protocols for oblivious linear function evaluation from ring-LWE

Juan Ramón Troncoso-Pastoriza

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f (x) = ax + b, where a sender inputs the field elements a, b, and a receiver inputs x and learns f (x). OLE can be used to build secret-shared multiplicatio ...

IOS PRESS2022

Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat

Bootstrapping parameters for the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17), are usually instantiated using sparse secrets to be efficient. However, using sparse secrets constrains the range of practical parameters withi ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Multiparty Homomorphic Encryption from Ring-Learning-with-Errors

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Christian Vincent Mouchet

We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with dishonest majority that is based on multiparty homomorphic encryption (MHE). To support our solution, we introduce a multiparty version of the Brakerski-Fa ...

2021

Revisiting Multivariate Ring Learning with Errors and Its Applications on Lattice-Based Cryptography

Juan Ramón Troncoso-Pastoriza

The “Multivariate Ring Learning with Errors” problem was presented as a generalization of Ring Learning with Errors (RLWE), introducing efficiency improvements with respect to the RLWE counterpart thanks to its multivariate structure. Nevertheless, the rec ...

2021

Crypt4GH: a file format standard enabling native access to encrypted data

Juan Ramón Troncoso-Pastoriza

Motivation: The majority of genome analysis tools and pipelines require data to be decrypted for access. This potentially leaves sensitive genetic data exposed, either because the unencrypted data is not removed after analysis, or because the data leaves t ...

OXFORD UNIV PRESS2021

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, David Jules Froelicher, Jean-Philippe Léonard Bossuat, Yiping Ma

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag"variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit ge ...

CELL PRESS2021

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, David Jules Froelicher, Apostolos Pyrgelis, Joao André Gomes de Sá e Sousa, Jean-Philippe Léonard Bossuat, Sinem Sav

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021